posted on 2020-10-20, 02:43authored byShipei Xing, Yan Hu, Zixuan Yin, Min Liu, Xiaoyu Tang, Mingliang Fang, Tao Huan
Spectral similarity comparison through
tandem mass spectrometry
(MS2) is a powerful approach to annotate known and unknown
metabolic features in mass spectrometry (MS)-based untargeted metabolomics.
In this work, we proposed the concept of hypothetical neutral loss
(HNL), which is the mass difference between a pair of fragment ions
in a MS2 spectrum. We demonstrated that HNL values contain
core structural information that can be used to accurately assess
the structural similarity between two MS2 spectra. We then
developed the Core Structure-based Search (CSS) algorithm based on
HNL values. CSS was validated with sets of hundreds of randomly selected
metabolites and their reference MS2 spectra, showing significantly
improved correlation between spectral and structural similarities.
Compared to state-of-the-art spectral similarity algorithms, CSS generates
better ranking of structurally relevant chemicals among false positives.
Combining CSS, HNL library, and biotransformation database, we further
developed Metabolite core structure-based Search (McSearch), a novel
computational solution to facilitate the annotation of unknown metabolites
using the reference MS2 spectra of their structural analogs.
McSearch generates better results in the Critical Assessment of Small
Molecule Identification (CASMI) 2017 data set than conventional unknown
feature annotation programs. McSearch was also tested in experimental
MS2 data of xenobiotic metabolite derivatives belonging
to three different metabolic pathways. Our results confirmed that
McSearch can better capture the underlying structural similarity between
MS2 spectra. Overall, this work provides a novel direction
for metabolite annotation via HNL values, paving the way for annotating
metabolites using their structurally similar compounds.